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Product Hunt analytics beyond the daily leaderboard Discussion | Link

The mobile source of truth Discussion | Link

Like a save/load game for your work Discussion | Link

Control robots in your browser — no setup needed Discussion | Link

I said "maybe a couple days" on a call last Tuesday. By Wednesday morning it was in a Jira ticket as "2 days." By Thursday afternoon somebody was checking in to see if we were tracking against the two day commitment. Nobody did anything wrong. The person who wrote it down was capturing what I said. The person checking in was doing their job. I was the one who said the words. The system worked exactly as designed. The system is the problem. Something Ive learned is that theres no such thing as a rough number in meetings today with all of the AI note takers... The moment you say a number out loud, it stops being a feeling and starts being a quote. The hedge in front of it doesnt survive the transcription. "Maybe" disappears. "Couple" gets rounded to a specific integer. "Give or take" is the first thing that hits the cutting room floor. What lands in the document is the number, naked, with no caveats and no error bars. Everyone in the meeting heard what you heard. They heard the hedge. They watched you wave your hands. They understood, in the moment, that you werent committing. But the document doesnt remember any of that. The document just remembers the number. And the document outlives the conversation, which is where all the nuance lived. Ive watched myself do this for years and I still get caught by it. Someone asks how long something will take. I want to be helpful. I want to seem confident. I want to keep the meeting moving. So I say a number. The number is approximately right, or at least I think it is, but I havent actually thought about it the way you would think about it if you were going to commit to it. By saying it out loud, Ive committed to it. The fix, if theres one, is to refuse the number. Not rudely. Just clearly. "I need to look at it before I give you a real number. I can have one for you by Friday." This works about half the time. The other half, somebody in the room is going to ask you for a ballpark anyway, and youre going to give them one, and t

Siri is now directly embedded into the camera app, and there are more artificial intelligence tools in the Photos app to alter your images.

If you work with Brazilian companies — as an accountant, credit analyst, or anyone processing PJ clients at scale — here's a practical automation approach using free public data. What you can verify automatically For any CNPJ, public data gives you: Situação cadastral : ATIVA, BAIXADA, INAPTA, SUSPENSA — critical for invoice validation Razão social : legal name for contract matching CNAE : is this company allowed to do what they claim? QSA : who are the actual partners/directors? Data abertura : how old is the company? The data 65M+ CNPJs from Receita Federal, indexed and searchable at Jurídico Online . Free. Also available as a Python package: pip install juridico-online from juridico_online import empresa_url , buscar_url # Get company page URL for a CNPJ url = empresa_url ( " 00.000.000/0001-91 " ) print ( url ) # https://juridicoonline.com.br/empresa/00000000000191 # Search by company or partner name search = buscar_url ( " Magazine Luiza " ) print ( search ) Checks worth automating 1. Situação ATIVA before accepting any invoice INAPTA or BAIXADA companies cannot legally issue NF-e. 2. CNAE vs service being billed A company with CNAE "comércio de alimentos" billing for software development is a red flag. 3. Company age vs contract value A 3-month-old company offering a R$500k contract deserves extra scrutiny. 4. Shared partners across suppliers If two suppliers share directors, that's a conflict of interest. Search partner names at juridicoonline.com.br to see all companies they control. Integration patterns ERP/AP : validate CNPJ status before releasing payment Onboarding : auto-fill razão social when client enters CNPJ Batch audit : cross-check your vendor list quarterly Monitoring : alert if a key supplier's CNPJ changes status The data is public, free, and updated regularly. No excuse to check manually at scale.

Been doing a lot of competitive research for clients lately — checking hashtag volumes, tracking top posts in a niche, that kind of thing. Kept switching between Claude Code and browser tabs to cross-reference stuff manually. Got annoying fast. Found hikerapi-mcp, a Model Context Protocol server that exposes 100+ Instagram endpoints as tools directly inside Claude Code. Figured I'd try it. Setup was straightforward. The one thing I did differently was keeping the API key out of config files entirely — passed it as an environment variable instead. Smaller attack surface if I accidentally commit something. Also filtered down the tool groups with HIKERAPI_TAGS because 100+ tools showing up in context is chaos. I only need hashtag search and competitor profile data, so I scoped it to just those. "env": { "HIKERAPI_KEY": "${HIKERAPI_KEY}", "HIKERAPI_TAGS": "User Profile,Post Details,Search,Hashtags,Stories" } One thing that tripped me up for a solid 20 minutes: HikerAPI runs on a prepaid model (credits in rubles). If your balance is zero, you get HTTP 402, not 401. I kept thinking my key was invalid and regenerated it twice before I figured out I just needed to top up. Once that was sorted, it actually works well. Now I can ask things like "what are the top 10 posts for #socialmediamarketing this week" or pull a competitor's recent content directly in the same session where I'm building the campaign strategy. Cuts out a lot of context switching. Repo if you want to check it out: github.com/subzeroid/hikerapi-mcp Wrote up the full setup with config details here if useful: https://dev.to/simrp360/querying-instagram-from-claude-code-wiring-up-hikerapis-mcp-server-57jf Anyone else using MCP servers for social data research? Curious what other setups people are running.

Most people pick a model the wrong way around. They look at the leaderboard, see Opus on top, and reach for it by default. Sonnet if they want to save money. Haiku almost never, because the name says "small." That habit costs you. For a lot of what you actually build, Haiku is the right call, and you're paying three to five times more for capability the task never uses. This post is about how to choose, and why Haiku should be your default more often than it is. The short version: don't start from "what's the best model." Start from "what does this task need." Most tasks don't need much. Comparison Here is the current lineup, with the numbers that matter when you're choosing. Haiku 4.5 Sonnet 4.6 Opus 4.8 Model ID claude-haiku-4-5 claude-sonnet-4-6 claude-opus-4-8 Input price (per 1M tokens) $1 $3 $5 Output price (per 1M tokens) $5 $15 $25 Context window 200K 1M 1M Max output 64K 64K 128K Best at speed, volume balance hardest reasoning Two things jump out. First, price . Haiku input is a fifth of Opus and a third of Sonnet. Output is the same ratio. If you send a million tokens through Opus for $25 and the same work would have been fine on Haiku, you spent $20 for nothing. And that gap is per request, so it compounds. A feature that runs ten thousand times a day on Opus instead of Haiku is not a rounding error. It is the difference between a feature that ships and one that gets cut for cost. Second, the context window . This is where Haiku gives something up: 200K tokens instead of 1M. That is the real tradeoff, and it points straight at when to use it. We'll come back to that. The mental model Stop ranking models. Rank tasks . Ask three questions about the task in front of you: Does it need real reasoning, or is it bounded? A task is bounded when a competent junior could do it from a clear spec without much judgment: pull these fields out, sort this into one of five buckets, rewrite this in a different tone, answer this from the text I gave you. A task needs reason

AI Health Companion for Chronic Illness Discussion | Link

E-signatures API for your app averaging ~3¢ per envelope Discussion | Link

Automatically organize, rename, and manage files with AI Discussion | Link

The popular wildfire tracking app is adding flood monitoring to its platform. It’s the first new disaster alert on the service, with many more to come.

Give your screen recordings more charm Discussion | Link

When we build something complex—whether it’s a skyscraper, a gourmet meal, or a piece of software—we usually start with a plan. In software development, however, it’s easy to skip that step. We often jump straight into implementation, focusing on how to write the code instead of the intent behind it. Over time, this leads to rework, confusion, and systems that don't quite match our original goals. Spec-Driven Development (SDD) is an approach that shifts the focus back to the plan. Instead of starting with code, you start with a Specification : a clear, structured description of what the software should do. You then use an AI coding agent as a high-speed collaborator to help turn that specification into working code. 🔍 What is a “Spec”? A Specification (or “Spec”) is a written contract between your intention and the final product. It isn't a 50-page manual; it's a living document that defines: What the system should do. How it should behave in different scenarios. Which constraints and rules it must follow. From Prompts to Specifications There is a massive difference between a vague prompt and a structured spec. Loose prompts often lead to inconsistent results and "hallucinations," whereas clear specifications give the AI a much better target to hit. Bad Prompt: > “Build me a login system.” Good Spec: A good spec provides the clarity an AI (or a human) needs to succeed. You don’t need a 10-page document to benefit from specs; you need clarity, not length. 🛠️ Example Spec: Login Endpoint Overview Allow users to log in using email and password. Endpoint POST /api/login Request { "email" : "user@example.com" , "password" : "string" } Behavior Success: If email and password are correct → return a token and user info. Invalid Credentials: If credentials don't match → return INVALID_CREDENTIALS . Invalid Input: If fields are empty or the email format is wrong → return INVALID_INPUT . Rules Passwords must be stored hashed (e.g., bcrypt). Token expires in 24 hours. Security:

Lenovo's IdeaPad Slim 5x just might be the best laptop you can buy for $850. It's hard to find any major flaws, which is basically unheard of in a laptop at this price.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

While vibecoding, you sometimes need some background music. But music can also be a massive...

Turn ideas & designs into stunning video/animation. Discussion | Link

TL;DR: I built PackagePal — paste in any package from any language, pick your target language, and AI instantly finds the equivalent. No more Googling "what's the Node.js version of Python's requests ?" The Problem That Drove Me Crazy You know that moment when you're migrating a project — or just jumping between ecosystems — and you hit a wall trying to find the right package? I do. Every time. # You're used to this in Python import requests response = requests . get ( " https://api.example.com/data " ) And you move to Node.js and think: "Okay, what do I use here? axios? node-fetch? got? undici?" So you Google it. You find a Stack Overflow thread from 2019. Half the answers recommend packages that are now deprecated. You open 6 tabs. 20 minutes later you're still not sure which one is the current best choice. This wasn't a once-in-a-while thing for me. It happened constantly — switching between Python, JavaScript, Go, and Ruby on different projects. I was wasting real hours on a problem that felt completely solvable. So I built PackagePal . What PackagePal Does PackagePal uses AI to understand what a package actually does — its purpose, not just its name — and finds the best equivalent in whatever language you're moving to. The key insight: this isn't a lookup table. A simple mapping of requests → axios misses context. What if you're using requests for its session management? Or its retry logic? PackagePal surfaces options and explains why each one is a good match. Example searches people use it for: Python's pandas → JavaScript Ruby's devise → Node.js Go's cobra → Python JavaScript's lodash → Go Just type the package, pick the target language, and get results in seconds. 👉 Try it: packagepal.dev How I Built It Tech Stack 🤖 AI: Gemini Pro — handles the semantic understanding of what a package does and why an alternative matches ⚛️ Frontend: React + TypeScript ⚙️ Backend: Node.js + TypeScript on Google Cloud ⚡ Caching: Redis — so repeat searches (e.g., "requests → No